Search Results for "vs" - Page 2
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Showing 54 open source projects for "vs"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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SuperDesign
AI Product Design Agent
SuperDesign is an innovative open-source AI design agent that embeds directly into your integrated development environment (IDE), enabling developers and designers to generate product interface designs from natural language prompts. It works seamlessly inside environments like VS Code, Cursor, Windsurf, and Claude Code, eliminating the need to switch between design tools and code editors. With a single prompt, SuperDesign can produce full UI mockups, reusable components, and wireframes, dramatically speeding up the early stages of product creation and iteration. Beyond simple visual generation, it lets users fork and refine designs, supporting a dynamic workflow where ideas evolve quickly within the context of your project. ... -
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TurboDiffusion
100–200× Acceleration for Video Diffusion Models
TurboDiffusion is an advanced open-source framework designed to dramatically accelerate video diffusion model generation, aiming for performance improvements on the order of 100–200× compared with traditional implementations while retaining high output quality. It achieves this by combining a suite of algorithmic and engineering optimizations, including attention acceleration techniques, efficient step distillation methods, and quantization strategies that reduce computational overhead. The... -
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Deepnote
Deepnote is a drop-in replacement for Jupyter
Deepnote is an open-source collaborative data science notebook platform designed as a modern alternative to traditional Jupyter notebooks. The project provides an AI-first computational environment where users can write, analyze, and share code, data, and visualizations in a single integrated workspace. Built on top of the Jupyter kernel ecosystem, it maintains compatibility with existing notebook workflows while introducing additional features focused on collaboration and automation. The... -
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MobileCLIP
Implementation of "MobileCLIP" CVPR 2024
MobileCLIP is a family of efficient image-text embedding models designed for real-time, on-device retrieval and zero-shot classification. The repo provides training, inference, and evaluation code for MobileCLIP models trained on DataCompDR, and for newer MobileCLIP2 models trained on DFNDR. It includes an iOS demo app and Core ML artifacts to showcase practical, offline photo search and classification on iPhone-class hardware. Project notes highlight latency/accuracy trade-offs, with... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Watermark Anything
Official implementation of Watermark Anything with Localized Messages
Watermark Anything (WAM) is an advanced deep learning framework for embedding and detecting localized watermarks in digital images. Developed by Facebook Research, it provides a robust, flexible system that allows users to insert one or multiple watermarks within selected image regions while maintaining visual quality and recoverability. Unlike traditional watermarking methods that rely on uniform embedding, WAM supports spatially localized watermarks, enabling targeted protection of... -
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Taipy
Turns Data and AI algorithms into production-ready web applications
...Struggle with sluggish performance and excessive memory usage, as every data point demands processing. Large datasets become cumbersome, complicating the user experience and data analysis. Scenarios are made easy with Taipy Studio. A powerful VS Code extension that unlocks a convenient graphical editor. Get your methods invoked at a certain time or intervals. Enjoy a variety of predefined themes or build your own. -
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Janus
Unified Multimodal Understanding and Generation Models
Janus is a sophisticated open-source project from DeepSeek AI that aims to unify both visual understanding and image generation in a single model architecture. Rather than having separate systems for “look and describe” and “prompt and generate”, Janus uses an autoregressive transformer framework with a decoupled visual encoder—allowing it to ingest images for comprehension and to produce images from text prompts with shared internal representations. The design tackles long-standing... -
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swark.io
Create architecture diagrams from code automatically using LLMs
...Instead of relying on manually maintained diagrams that often become outdated, Swark uses AI to infer architecture patterns dynamically from the code itself. The tool integrates with GitHub Copilot and the VS Code environment, allowing developers to generate diagrams with minimal setup and without requiring additional authentication or API configuration. Because the logic of understanding code structure is handled by an LLM, Swark can support many programming languages and frameworks without requiring custom rules for each language. -
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OpenSumi
A framework helps you quickly build Cloud or Desktop IDE products
A framework helps you quickly build Cloud or Desktop IDE products. Integrate with your coding frameworks with ease. Support the container, Electron and front-end frameworks. Also help to ship and deploy quickly. Support VS Code plugins, OpenSumi plugins and OpenSumi modules to meet various business requirements. Customize the UI design in any way you like, no matter to simply configure the built-in UI, or develop a UI template, or build your own UI through plugins. OpenSumi framework aims to solve the redundant building problem of IDE product development within Alibaba, endeavours to fulfill IDE customization capabilities in more vertical scenarios and implement the shared underlying layer of Web and local clients, so that IDE development can move from the early "slash-and-burn" era to the "machine-based mass production" era. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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GLM-4.5V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
GLM-4.5V is the preceding iteration in the GLM-V series that laid much of the groundwork for general multimodal reasoning and vision-language understanding. It embodies the design philosophy of mixing visual and textual modalities into a unified model capable of general-purpose reasoning, content understanding, and generation, while already supporting a wide variety of tasks: from image captioning and visual question answering to content recognition, GUI-based agents, video understanding,... -
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CC Workflow Studio
Accelerate Claude Code/GitHub Copilot
CC Workflow Studio is a powerful Visual Studio Code extension that accelerates AI-assisted development by providing a visual workflow editor tailored for AI automation and agent orchestration, particularly with tools like Claude Code, GitHub Copilot, OpenAI Codex, and others. The extension lets developers and creators design complex AI workflows using intuitive drag-and-drop canvases or via conversational AI commands, blending graphical editing with natural language refinement. Workflows can... -
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FlashMLA
FlashMLA: Efficient Multi-head Latent Attention Kernels
FlashMLA is a high-performance decoding kernel library designed especially for Multi-Head Latent Attention (MLA) workloads, targeting NVIDIA Hopper GPU architectures. It provides optimized kernels for MLA decoding, including support for variable-length sequences, helping reduce latency and increase throughput in model inference systems using that attention style. The library supports both BF16 and FP16 data types, and includes a paged KV cache implementation with a block size of 64 to... -
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SHAP
A game theoretic approach to explain the output of ml models
...Fast C++ implementations are supported for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark tree models. To understand how a single feature effects the output of the model we can plot the SHAP value of that feature vs. the value of the feature for all the examples in a dataset. Since SHAP values represent a feature's responsibility for a change in the model output, the plot below represents the change in predicted house price as RM (the average number of rooms per house in an area) changes. -
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Controllable-RAG-Agent
This repository provides an advanced RAG
Controllable-RAG-Agent is an advanced Retrieval-Augmented Generation (RAG) system designed specifically for complex, multi-step question answering over your own documents. Instead of relying solely on simple semantic search, it builds a deterministic control graph that acts as the “brain” of the agent, orchestrating planning, retrieval, reasoning, and verification across many steps. The pipeline ingests PDFs, splits them into chapters, cleans and preprocesses text, then constructs vector... -
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tic tac toe AI
simplest AI programme of tic-tac-toe game
This is a program of tic tac toe game it currently is the 1.0 version of this this is my program - an AI program which plays tic-tac-toe, it is an AI program which is given knowledge on the basis of my previous analysis and knowledge about playing tic-tac-toe. I have made it to be playable with players right now but I can make it for AI vs AI, AI vs player, player vs player as well. Using a settings option. I think this program has enough IQ to defeat a normal person. This is the update 1.1 of this game. My future visions about this program is: v 1.0.1 --> bug fixes v 1.1 --> (added) click interaction _______________________________________________________________________________________________________________________________________________ v 1.2 --> addition of reinforcement learning (cache data different for each computer unlike v1.3). ... -
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Automated Interpretability
Code for Language models can explain neurons in language models paper
The automated-interpretability repository implements tools and pipelines for automatically generating, simulating, and scoring explanations of neuron (or latent feature) behavior in neural networks. Instead of relying purely on manual, ad hoc interpretability probing, this repo aims to scale interpretability by using algorithmic methods that produce candidate explanations and assess their quality. It includes a “neuron explainer” component that, given a target neuron or latent feature,... -
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GPT-2 Output Dataset
Dataset of GPT-2 outputs for research in detection, biases, and more
The GPT-2 Output Dataset is a large collection of model-generated text, released by OpenAI alongside the GPT-2 research paper to study the behaviors and limitations of large language models. It contains 250,000 samples of GPT-2 outputs, generated with different sampling strategies such as top-k truncation, to highlight the diversity and quality of model completions. The dataset also includes corresponding human-written text for comparison, enabling researchers to explore methods for... -
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AI Explainability 360
Interpretability and explainability of data and machine learning model
...The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. -
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Codiga VS Code
VS Code plugin that suggests code blocks as you type and check
VS Code plugin that suggests code blocks as you type and check for errors. Works for JavaScript, TypeScript, Python, Java, Scala, Ruby, PHP, Apex, Docker. -
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Point-E
Point cloud diffusion for 3D model synthesis
point-e is the official repository for Point-E, a generative model developed by OpenAI that produces 3D point clouds from textual (or image) prompts. Its principal advantage is speed: it can generate 3D assets in just 1–2 minutes on a single GPU, which is significantly faster than many competing text-to-3D models. The model works via a two-stage diffusion approach: first, it uses a text → image diffusion network to produce a synthetic 2D view consistent with the prompt; then a second... -
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Guided Diffusion
Codebase for Diffusion Models Beat GANS on Image Synthesis
...The code provides model definitions (UNet, diffusion schedules), sampling and training scripts, and utilities for guidance and evaluation. A key insight is that combining diffusion sampling with classifier gradients allows fine control over the generated images, trading off diversity vs fidelity. The repository includes scripts such as image_train.py, image_sample.py, and classifier_train.py to train diffusion models, generate samples, and train guiding classifiers. It also ships with precomputed evaluation batches and baseline comparisons to support reproducible benchmarking of new models. -
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Following Instructions with Feedback
Training Language Models to Follow Instructions with Human Feedback
The following-instructions-human-feedback repository contains the code and supplementary materials underpinning OpenAI’s work in training language models (InstructGPT models) that better follow user instructions through human feedback. The repo hosts the model card, sample automatic evaluation outputs, and labeling guidelines used in the process. It is explicitly tied to the “Training language models to follow instructions with human feedback” paper, and serves as a reference for how OpenAI... -
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Procgen
Procedurally-Generated Game-Like Gym-Environments
...The environments are designed to run very quickly (thousands of steps per second on a single core) to facilitate large-scale experiments and make benchmarking efficient. The benchmark supports both “easy” and “hard” difficulty modes, letting researchers trade off computational cost vs challenge. The repo provides a C++ core for game logic and rendering (with support for gym/Gym3 wrappers) plus Python bindings and interactive mode for human play testing. -
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ML workspace
All-in-one web-based IDE specialized for machine learning
...This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. Easy to deploy on Mac, Linux, and Windows via Docker. Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.By default, the workspace container has no resource constraints and can use as much of a given resource as the host’s kernel scheduler allows. -
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End-to-End Negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
End-to-End Negotiator is a PyTorch-based research framework developed by Facebook AI Research to train neural agents capable of conducting strategic negotiations in natural language. The project implements the models presented in two key papers: “Deal or No Deal? End-to-End Learning for Negotiation Dialogues” and “Hierarchical Text Generation and Planning for Strategic Dialogue”. It enables agents to plan, reason, and communicate effectively to maximize outcomes in multi-turn negotiations...
